Microbial Ecology

, Volume 71, Issue 2, pp 482–493 | Cite as

Soil Parameters Drive the Structure, Diversity and Metabolic Potentials of the Bacterial Communities Across Temperate Beech Forest Soil Sequences

  • M. Jeanbille
  • M. Buée
  • C. Bach
  • A. Cébron
  • P. Frey-Klett
  • M. P. Turpault
  • S. UrozEmail author
Soil Microbiology


Soil and climatic conditions as well as land cover and land management have been shown to strongly impact the structure and diversity of the soil bacterial communities. Here, we addressed under a same land cover the potential effect of the edaphic parameters on the soil bacterial communities, excluding potential confounding factors as climate. To do this, we characterized two natural soil sequences occurring in the Montiers experimental site. Spatially distant soil samples were collected below Fagus sylvatica tree stands to assess the effect of soil sequences on the edaphic parameters, as well as the structure and diversity of the bacterial communities. Soil analyses revealed that the two soil sequences were characterized by higher pH and calcium and magnesium contents in the lower plots. Metabolic assays based on Biolog Ecoplates highlighted higher intensity and richness in usable carbon substrates in the lower plots than in the middle and upper plots, although no significant differences occurred in the abundance of bacterial and fungal communities along the soil sequences as assessed using quantitative PCR. Pyrosequencing analysis of 16S ribosomal RNA (rRNA) gene amplicons revealed that Proteobacteria, Acidobacteria and Bacteroidetes were the most abundantly represented phyla. Acidobacteria, Proteobacteria and Chlamydiae were significantly enriched in the most acidic and nutrient-poor soils compared to the Bacteroidetes, which were significantly enriched in the soils presenting the higher pH and nutrient contents. Interestingly, aluminium, nitrogen, calcium, nutrient availability and pH appeared to be the best predictors of the bacterial community structures along the soil sequences.


Bacterial communities Forest soil Soil type Fagus sylvatica Nutrient availability Soil pH 



This work was funded by grants from the ANDRA (Agence Nationale pour la Gestion des Déchets Radioactifs), the ANR JC ‘Bactoweather’ (ANR-11-JSV7-0001) and partly by the Laboratory of Excellence Arbre (ANR-11-LABX-0002-01; INABACT project). M. Jeanbille was a Master student supported by a fellowship from the ANDRA. We thank F. Martin, Y. Colin and E. Morin for helpful discussions, S. Didier and C. Calvaruso for technical assistance and the Office National des Forêts (ONF) for the management of the forest experimental site of Montiers. We would like to thank the reviewers for their implication in the improvement of our manuscript.

Supplementary material

248_2015_669_MOESM1_ESM.doc (461 kb)
Fig. S1 Site description and sample location. The two soil sequences (SS) considered (SS1 and SS2) are presented as well as the GPS coordinates of each soil sample (DOC 461 kb)
248_2015_669_MOESM2_ESM.doc (92 kb)
Fig. S2 Multivariate analysis of the BIOLOG data. In this analysis, principal component axis 1 and 2 explain most of the variance in the data cumulatively (F1 = 32.87 % and F2 = 12.22 %). Treatments are presented as follow: U, upper plots; M, middle plots and L, lower plots. Two soil sequences (SS) have been considered: SS1 and SS2 (DOC 91 kb)
248_2015_669_MOESM3_ESM.doc (110 kb)
Fig. S3 Metabolic and taxonomic Shannon diversity index performed for SS1 (A) and SS2 (B). For each soil sequence, data from the replicates coming from the Upper (U), Middle (M) and Lower (L) plots have been considered. For each index, significant differences are presented by different letters (P < 0.05) (DOC 110 kb)
248_2015_669_MOESM4_ESM.doc (110 kb)
Fig. S4 Multivariate analysis describing the relationships between the taxa and the metabolic potentials. In this analysis, principal component axis 1 and 2 explain most of the variance in the data cumulatively (F1 = 40.19 % and F2 = 15.88 %). Treatments are presented as follow: U, upper plots; M, middle plots and L, lower plots (DOC 109 kb)
248_2015_669_MOESM5_ESM.doc (56 kb)
Table S1 Physico-chemical characteristics of the soil depth sampled (5-20 cm) in each plot and soil sequence (SS1 and SS2). A. Chemical characteristics B. Physical characteristics (Turpault M-P., personal communication) (DOC 56 kb)
248_2015_669_MOESM6_ESM.doc (102 kb)
Table S2 Pearson correlation between soil parameters and Biolog data on SS1 (A), SS2 (B) soil sequences or both (C) (DOC 101 kb)
248_2015_669_MOESM7_ESM.doc (64 kb)
Table S3 Pearson correlations between soil parameters and relative abundance of major phyla on SS1 (A), SS2 (B) soil sequences or both (C) (DOC 64 kb)
248_2015_669_MOESM8_ESM.doc (110 kb)
Table S4 Pearson correlation between BIOLOG data and relative abundance of major phyla on SS1 (A) and SS2 (B) soil sequences or both (C) (DOC 110 kb)
248_2015_669_MOESM9_ESM.doc (36 kb)
Table S5 Combined analyses of soil sequences (SS1 +SS2) at the phylum, class, order, family and genus levels (DOC 36 kb)


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • M. Jeanbille
    • 1
    • 2
  • M. Buée
    • 1
    • 2
  • C. Bach
    • 1
  • A. Cébron
    • 3
    • 4
  • P. Frey-Klett
    • 1
    • 2
  • M. P. Turpault
    • 2
  • S. Uroz
    • 1
    • 2
    • 5
    • 6
    Email author
  1. 1.INRA, UMR1136 Interactions Arbres-MicroorganismesChampenouxFrance
  2. 2.Université de Lorraine, UMR1136 Interactions Arbres-MicroorganismesVandoeuvre-lès-NancyFrance
  3. 3.INRA UR 1138 “Biogéochimie des Ecosystèmes Forestiers”, Centre INRA de NancyChampenouxFrance
  4. 4.CNRS, LIEC UMR7360 Faculté des Sciences et TechnologiesVandoeuvre-les-NancyFrance
  5. 5.Université de Lorraine, LIEC UMR7360 Faculté des Sciences et TechnologiesVandoeuvre-les-NancyFrance
  6. 6.UMR 1136 INRA-Université de Lorraine, Interactions Arbres Micro-organismesChampenouxFrance

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